With the widespread demand of video streaming, some of the limitations have to be overcome. For example, users often want to watch a video over the Internet having only a limited bandwidth for obtaining that video stream. For example, users night want to obtain the video stream over a mobile telephone connection or a home wireless connection. The lack of bandwidth can be compensated for by spending time downloading content to local storage so that, even with a very slow link, a large video stream can be obtained and stored locally for eventual play-out, if enough time is provided for the downloading. This is fine if the server or other provider of the video stream is provided advance notice of a request for the stream and can get enough of it to the local storage before the user starts to watch the video stream. Unfortunately, this is not an acceptable solution in many cases, because users often expect to start watching a video stream shortly after having decided what to watch.
A video stream (typically containing an image portion and an audio portion) can require considerable bandwidth, especially at high resolution, such as HD videos. Audio typically requires much less bandwidth, but still sometimes needs to be taken into account. One approach for streaming video (i.e., provide it to a local content consumer, such as a display device used by a user, in sufficient time to allow the consumption to start shortly after starting a session wherein a particular stream is requested) is to heavily compress the video stream. Typically, lossy compression compression that is not entirely reversible) provides more compression than lossless compression, but heavy lossy compression provides an undesirable user experience.
What is needed for video compression is to be able to reduce the number of bits required to represent a sequence of images without creating too many undesirable artifacts and to keep the perceived video quality as constant is possible. One simple approach of compression is to compress one frame and represent each subsequent frame by a difference frame representing the pixel-by-pixel difference between the frame being encoded and its prior frame, and compress the difference. An improvement over this is to identify scene changes in the video stream (e.g., where the camera suddenly changes from one view to another such that the difference between two frames on each side of a scene change is not as compressible as other difference frames) and encode each “group of pictures” separately.
There are various techniques to automatically identify scene changes, but often that is not enough to maximize and provide the compression needed to provide desirable user experiences given bandwidth constraints.
Human vision system (“HVS”) model has been utilized to deal with biological and psychological processes in imaging processing, video processing and computer vision. The model is used to simplify the behaviors of the complex visual system of a human. The HVS model is updated as the knowledge of the true visual system improves. Some assumption about the HVS model includes visual acuity based on color and motion, resolution resolving based on motion factors, and attention modeling using face recognition.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent upon a reading of the specification and a study of the drawings.